Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 2Citation - Scopus: 2Assessing Edible Composite Film Polymer From Potato Industry Effluent Under High Hydrostatic Pressure and Its Antimicrobial Properties(Wiley, 2022) Akdemir Evrendilek, Gülsün; Bulut, Nurullah; Uzuner, SibelDevelopment of edible film from potato industry effluent having antimicrobial properties against Salmonella enteritidis and Escherichia coli O157:H7 by addition of Citrus sinensis volatile oil (VO), and changes of its textural properties under high hydrostatic pressure (HHP) are investigated. The optimum operational conditions are determined as 500 MPa pressure, 36.97 µL VO, and 15 min processing time with the minimum force value of 372.33 × g. Textural properties are also modeled through empirical modeling, best fit Box-Behnken design, and artificial neuron network. Inhibition zones for Salmonella enteritidis and E. coli O157:H7 at the optimum HHP conditions are 1.50 ± 0.11 and 2.18 ± 0.07 cm, respectively. Textural properties of force and elongation at break of the HHP-processed films range from 2.27 ± 0.52 to 5.23 ± 0.38 N, and from 8.57 ± 1.31 to 13.36 ± 1.36 mm, respectively. Thermal transition of the edible film is observed at 87.42 °C for 7.36 min. Addition of C. sinensis VO improves the antimicrobial properties, whereas HHP improves the textural properties of the film. It is suggested that the developed film has potential to be used as an edible food packaging material.Article Citation - WoS: 11Citation - Scopus: 12Passenger Flows Estimation of Light Rail Transit (lrt) System in Izmir, Turkey Using Multiple Regression and Ann Methods(Faculty of Transport and Traffic Sciences, University of Zagreb, 2012) Özuysal, Mustafa; Tayfur, Gökmen; Tanyel, SerhanPassenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.Article Citation - WoS: 28Citation - Scopus: 18Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey(John Wiley and Sons Inc., 2010) İnal, FikretTropospheric (ground-level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross-validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d2) for the testing data were 0.90, 8.78 μg/m3, 11.15μg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non-linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. Tropospheric ozone has adverse effects on human health and environment. Here, the next-day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron type artificial neural networks (MLP-ANNs). The MLP-ANNs were compared to multiple linear and multiple non-linear regression models. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.Article Citation - WoS: 5Citation - Scopus: 8Forecasting Interregional Commodity Flows Using Artificial Neural Networks: an Evaluation(Taylor and Francis Ltd., 2004) Çelik, Hüseyin MuratPrevious studies have concluded that the use of artificial neural networks (ANNs) is a promising new technique for modelling freight distribution, supporting, the findings of other studies in the area of spatial interaction modelling. However, the forecasting performance of ANNs is still under investigation. This study tests the predictive performance of the ANN Model with respect to a Box-Cox spatial interaction model. It is concluded that the Box-Cox model outperforms ANN in forecasting interregional commodity flows even if ANN had proven calibration superiority in comparison to conventional gravity type models.